The early works in deep clustering usually simply combined feature learning with shallow clustering. For example, some methods combined the stacked auto-encoders (SAE) with the traditional clustering algorithms such as \(k\) -means [1]}, [2]}, [3]}, subspace clustering [4]} and spectral clustering [5]}, or combined the Convolutional Neural Network (CNN) with the hierarchical clustering [6]}. However, the above methods usually require post-processing to obtain the cluster assignments.
